Summary/Abstract In the presence of diseases such as ischemic heart disease (IHD), cardiac dyssynchrony deteriorates cardiac function and often cannot be treated effectively. However, while imaging methods such as cardiovascular magnetic resonance (CMR) can provide high quality images of the moving heart, conventional clinical quantitative analysis of cardiac function is largely limited to global function analysis of the left ventricle (LV), with only qualitative and subjective characterization of regional function. An obstacle to better quantification of regional function is the complex 3D structure and motion of the heart wall, which has typically necessitated time-consuming user-guided processing of the images to carry out the associated 3D-motion analysis. Recent advances in machine-learning (ML) approaches for image analysis are promising as new means to speed up the processing of cardiac images, as well as to analyze the underlying regional motion patterns. However, current Deep ML (DML) approaches to image analysis largely function as ?black boxes?, without clear indications of which features contribute most to the analysis results, thus limiting their clinical utility. In the initial funded period of this research project, we have been developing integrated approaches to the segmentation, 3D reconstruction, and analysis of CMR data, with application to the evaluation of cardiac dyssynchrony. Today, treatment of dyssynchrony in HF with cardiac resynchronization therapy (CRT) leads to improvement in only ~2/3 patients selected with conventional criteria (usually by electrocardiogram [ECG]). Our initial results show encouraging results of correlation between MRI evaluation of dyssynchrony and cardiac resynchronization therapy (CRT) outcomes. In the new proposed research, we will further develop these methods, with the goal of automating the cardiac analysis methods. This will include the introduction of new ML-based methods, which will incorporate information on the specific cardiac motion factors that lead to classification of different disease states in dyssynchrony. Our Hypothesis is that by using these new ML-based methods for cardiac motion analysis, we will discover and evaluate significant quantitative correlations between different cardiac dyssynchrony motion patterns and CRT outcomes. Also, late-gadolinium enhancement (LGE) provides images for infarction visualization. Incorporation of tissue characterization into the motion-pattern analysis could lead to increased understanding of how infarcted areas affect regional motion in concert with dyssynchrony. The unearthing of these findings will allow us to validate them in future clinical studies. The project will also disseminate our novel, coupled DML and model-based methodology for quantifying and classifying cardiac motion in diseases affecting regional wall motion. Other research groups can then apply our tools to specifically study dyssynchrony, as well as other cardiac diseases affecting LV motion.